EpiModel

Mathematical Modeling of Infectious Disease Dynamics

EpiModel is an R package that provides tools for simulating and analyzing
mathematical models of infectious disease dynamics. Supported epidemic model classes
include deterministic compartmental models, stochastic individual contact
models, and stochastic network models. Disease types include SI, SIR, and
SIS epidemics with and without demography, with utilities available for
expansion to construct and simulate epidemic models of arbitrary complexity.
The network model class is based on the statistical framework of temporal
exponential random graph models (ERGMs) implementated in the
Statnet suite of software
for R.

Installation

The current software version is EpiModel v1.7.2, which
may be downloaded from
CRAN
and installed in R through:

install.packages("EpiModel", dependencies = TRUE)

The development version of EpiModel is hosted on
GitHub and may be
installed via the remotes package by:

remotes::install_github("statnet/EpiModel")

The software source code is available at the
Github Repository.
Users should submit bug reports and feature requests as issues there.
The Releases
page on the repository lists all the changes to the software over time.

Citation

If using EpiModel for teaching or research, please include a citation of our software with:

Getting Started

Software Manual

The
EpiModel Software Manual provides a list of all the main functions
within the package, with syntax and examples. This documentation is
also available within the package by consulting the help files.

Methods Paper

Next Steps

The Tutorials page provides introductions to
running epidemic models of the three classes supported in EpiModel, and
then expanding those models to address novel research questions. For
greater theoretical background to fitting stochastic network models
specifically, consult the Workshops page to view
the materials from our in-person courses on using EpiModel.

News

Trainings

NME is a 5-day short course at the University of
Washington that provides an introduction to stochastic network models for infectious
disease transmission dynamics, with a focus on empirically based modeling of HIV, STIs,
and other close-contact infectious diseases. For more information on how to apply for
the course, see our course website.